Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.

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Analysis of the Influence of Waves in the Occurrence of Accidents in the Portuguese Coast Using Bayesian Belief Networks

Journal of Konbin ◽

10.2478/v10040-008-0140-5 ◽

2010 ◽

Vol 13 (1) ◽

pp. 105-116 ◽

Cited By ~ 6

Author(s):

Pedro Antão ◽

C. Soares

Keyword(s):

Weather Conditions ◽

Bayesian Belief Network ◽

Bayesian Belief Networks ◽

Belief Networks ◽

Large Wave ◽

Simple Modification ◽

Portuguese Coast ◽

Lack Of Information ◽

Wave Heights ◽

Height Data

Analysis of the Influence of Waves in the Occurrence of Accidents in the Portuguese Coast Using Bayesian Belief NetworksSea and weather conditions are the second most frequent cause of accidents in Portuguese waters accounting for 23% of the occurrences. However due to lack of information in the Portuguese maritime accident database it is difficult to assess what this cause consists specifically, i.e., fog, large wave heights or other events. In the present study significant wave height data was introduced in a Bayesian Belief Network (BBN) model in order assess the correlation between their amplitude and certain accident typologies and related consequences (human injuries or fatalities). The results of different inferences of the BBN model show that through a simple modification of an accident model the influence of weather pattern can be assessed and specific risk factors can be identified.

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Path Associative Networks and Bayesian Belief Networks A career decision support tool

PsycEXTRA Dataset ◽

10.1037/e683322011-113 ◽

2000 ◽

Author(s):

Clarence C. Rohrbaugh

Keyword(s):

Decision Support ◽

Decision Support Tool ◽

Career Decision ◽

Bayesian Belief Networks ◽

Belief Networks ◽

Associative Networks ◽

Support Tool

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Using Bayesian network models to incorporate uncertainty in the economic analysis of pollution abatement measures under the water framework directive

Water Science & Technology Water Supply ◽

10.2166/ws.2005.0054 ◽

2005 ◽

Vol 5 (6) ◽

pp. 95-104 ◽

Cited By ~ 4

Author(s):

D.N. Barton ◽

T. Saloranta ◽

T.H. Bakken ◽

A. Lyche Solheim ◽

J. Moe ◽

...

Keyword(s):

Water Quality ◽

At Risk ◽

Economic Valuation ◽

Pollution Abatement ◽

Network Models ◽

Cost Effective ◽

Bayesian Belief Networks ◽

Probabilistic Interpretation ◽

Belief Networks ◽

Benefit Cost Analysis

The evaluation of water bodies “at risk” of not achieving the Water Framework Directive's (WFD) goal of “good status” begs the question of how big a risk is acceptable before a programme of measures should be implemented. Documentation of expert judgement and statistical uncertainty in pollution budgets and water quality modelling, combined with Monte Carlo simulation and Bayesian belief networks, make it possible to give a probabilistic interpretation of “at risk”. Combined with information on abatement costs, a cost-effective ranking of measures based on expected costs and effect can be undertaken. Combined with economic valuation of water quality, the definition of “disproportionate cost” of abatement measures compared to benefits of achieving “good status” can also be given a probabilistic interpretation. Explicit modelling of uncertainty helps visualize where research and consulting efforts are most critical for reducing uncertainty. Based on data from the Morsa catchment in South-Eastern Norway, this paper discusses the relative merits of using Bayesian belief networks when integrating biophysical modelling results in the benefit-cost analysis of derogations and cost-effectiveness ranking of abatement measures under the WFD.

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Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

Landscape Ecology ◽

10.1007/s10980-021-01307-6 ◽

2021 ◽

Author(s):

James D. Karimi ◽

Jim A. Harris ◽

Ron Corstanje

Keyword(s):

Functional Connectivity ◽

Ecosystem Service ◽

Urban Areas ◽

Landscape Connectivity ◽

Structural Characteristics ◽

Bird Species ◽

Bayesian Belief Networks ◽

Belief Networks ◽

Patch Area ◽

Trade Offs

Abstract Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management.

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Modeling Attribution of Cyber Attacks Using Bayesian Belief Networks

Strategic Analysis ◽

10.1080/09700161.2020.1870268 ◽

2021 ◽

pp. 1-20

Author(s):

Munish Sharma

Keyword(s):

Bayesian Belief Networks ◽

Cyber Attacks ◽

Belief Networks

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The wildcat Felis silvestris in northern Turkey: assessment of status using camera trapping

Oryx ◽

10.1017/s0030605310001328 ◽

2011 ◽

Vol 45 (1) ◽

pp. 112-118 ◽

Cited By ~ 11

Author(s):

Özgün Emre Can ◽

İrfan Kandemi̇r ◽

İnci̇ Togan

Keyword(s):

Population Size ◽

Camera Traps ◽

Sampling Effort ◽

Felis Silvestris ◽

Forest Patch ◽

Western Turkey ◽

Lack Of Information ◽

The Status ◽

Capture Recapture ◽

Estimate Population Size

AbstractThe wildcat Felis silvestris is a protected species in Turkey but the lack of information on its status is an obstacle to conservation initiatives. To assess the status of the species we interviewed local forestry and wildlife personnel and conducted field surveys in selected sites in northern, eastern and western Turkey during 2000–2007. In January–May 2006 we surveyed for the wildcat using 16 passive infrared-trigged camera traps in Yaylacı k Research Forest, a 50-km2 forest patch in Yenice Forest in northern Turkey. A total sampling effort of 1,200 camera trap days over 40 km2 yielded photo-captures of eight individual wildcats over five sampling occasions. Using the software MARK to estimate population size the closed capture–recapture model M0, which assumes a constant capture probability among all occasions and individuals, best fitted the capture history data. The wildcat population size in Yaylacı k Research Forest was estimated to be 11 (confidence interval 9–23). Yenice Forest is probably one of the most important areas for the long-term conservation of the wildcat as it is the largest intact forest habitat in Turkey with little human presence, and without human settlements, and with a high diversity of prey species. However, it has been a major logging area and is not protected. The future of Yenice Forest and its wildcat population could be secured by granting this region a protection status and enforcing environmental legislation.

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